code: def testSeparableConv1d(self, random_normal_initializer_mock):
      graph = mtf.Graph()
      mesh = mtf.Mesh(graph, "my_mesh")
  
      depth_dim = mtf.Dimension("depth", 2)
      length_dim = mtf.Dimension("length", 4)
      output_dim = mtf.Dimension("output", 2)
  
      x = tf.constant(
          [[1, 0], [0, 1], [1, 1], [2, 1]], dtype=tf.float32
      )
      mtf_x = mtf.import_tf_tensor(
          mesh, x, shape=mtf.Shape([length_dim, depth_dim])
      )
  
      initializer_mock = mock.MagicMock()
      random_normal_initializer_mock.return_value = initializer_mock
      initializer_mock.side_effect = initialize_by_shape(
          {
              (2,): [1, 2],
              (2, 2): [[1, 0], [1, -1]],
          }
      )
  
      mtf_output = mtf.layers.separable_conv1d(
          mtf_x,
          output_dim,
          min_relative_pos=-1,
          max_relative_pos=1,
          use_bias=True,
      )
  
      mesh_impl = mtf.placement_mesh_impl.PlacementMeshImpl(
          shape=[], layout={}, devices=[""]
      )
      lowering = mtf.Lowering(graph, {mesh: mesh_impl})
      actual_output = lowering.export_to_tf_tensor(mtf_output)
  
      self.evaluate(tf.global_variables_initializer())
      self.evaluate(lowering.copy_masters_to_slices())
      actual = self.evaluate(actual_output)
  
      self.assertAllClose(actual, [[3, -2], [6, -4], [9, -6], [7, -4]])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import mesh_tensorflow
  from mesh_tensorflow import test_utils
  import mock
  import numpy
  import tensorflow.compat.v1
  import tensorflow_probability
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 15 for calling the LayersTest obj's underlying member method code with example usage: obj.testSeparableConv1d(random_normal_initializer_mock) without return types